Bridging the Evaluation Gap: Standardized Benchmarks for Multi-Objective Search

2026-03-25

AI Summary

This work addresses the long-standing absence of a unified evaluation benchmark in multi-objective search, where existing datasets suffer from highly correlated objectives and simplistic structures that fail to capture diverse Pareto frontiers. To bridge this gap, we introduce the first standardized benchmark suite encompassing four distinct scenarios: real-world road networks, synthetic graphs, game grids, and high-dimensional robotic paths. The suite systematically integrates objective relationships ranging from strongly correlated to fully independent, and provides fixed graph instances, standardized queries, and high-quality reference Pareto-optimal solution sets. This benchmark enables fair, reproducible comparisons between exact and approximate algorithms, substantially enhancing evaluation consistency, result comparability, and robustness in multi-objective search research.

Technology Category

• Search and Optimization: Combinatorial Optimization
• Constraint Satisfaction and Optimization: Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization

Abstract

Empirical evaluation in multi-objective search (MOS) has historically suffered from fragmentation, relying on heterogeneous problem instances with incompatible objective definitions that make cross-study comparisons difficult. This standardization gap is further exacerbated by the realization that DIMACS road networks, a historical default benchmark for the field, exhibit highly correlated objectives that fail to capture diverse Pareto-front structures. To address this, we introduce the first comprehensive, standardized benchmark suite for exact and approximate MOS. Our suite spans four structurally diverse domains: real-world road networks, structured synthetic graphs, game-based grid environments, and high-dimensional robotic motion-planning roadmaps. By providing fixed graph instances, standardized start-goal queries, and both exact and approximate reference Pareto-optimal solution sets, this suite captures a full spectrum of objective interactions: from strongly correlated to strictly independent. Ultimately, this benchmark provides a common foundation to ensure future MOS evaluations are robust, reproducible, and structurally comprehensive.

Problem

• multi-objective search
• benchmark standardization
• Pareto-front diversity
• empirical evaluation
• objective correlation

Innovation

• multi-objective search
• standardized benchmark
• Pareto-optimal solutions
• objective correlation
• reproducible evaluation